In recent years, there has been a lot of interest in algorithms that
learn feature hierarchies from unlabeled data. Deep learning
methods such as deep belief networks, sparse coding-based methods,
convolutional networks, and deep Boltzmann machines, have shown
promise and have already been successfully applied to a variety of
tasks in computer vision, audio processing, natural language
processing, information retrieval, and robotics. In this workshop, we
will bring together researchers who are interested in deep learning
and unsupervised feature learning, review the recent technical
progress, discuss the challenges, and identify promising future
research directions.

The workshop invites paper submissions that will be either presented
as oral or in poster format. Through invited talks, panel discussions
and presentations by the participants, this workshop attempts to
address some of the more controversial topics in deep learning today,
such as whether hierarchical systems are more powerful, and what
principles should guide the design of objective functions used to
train these models. Panel discussions will be led by the members of
the organizing committee as well as by prominent representatives of
the vision and neuro-science communities.

The goal of this workshop is two-fold. First, we want to identify the
next big challenges and propose research directions for the deep
learning community. Second, we want to bridge the gap between
researchers working on different (but related) fields, to leverage
their expertise, and to encourage the exchange of ideas with all the
other members of the NIPS community.

We solicit submissions of unpublished research papers. Papers must have at most 8 pages (with an additional ninth page allowed for cited references only), and must satisfy the formatting instructions of the NIPS 2010 call for papers; style files available here.